Overview

Dataset statistics

Number of variables17
Number of observations13840
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory577.8 B

Variable types

Numeric9
Categorical4
Boolean4

Alerts

Height is highly overall correlated with GenderHigh correlation
Weight is highly overall correlated with Gender and 1 other fieldsHigh correlation
Gender is highly overall correlated with Height and 1 other fieldsHigh correlation
family_history_with_overweight is highly overall correlated with WeightHigh correlation
FAVC is highly imbalanced (56.1%)Imbalance
CAEC is highly imbalanced (60.8%)Imbalance
SMOKE is highly imbalanced (90.0%)Imbalance
SCC is highly imbalanced (78.8%)Imbalance
CALC is highly imbalanced (51.1%)Imbalance
MTRANS is highly imbalanced (64.0%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique
FAF has 3434 (24.8%) zerosZeros
TUE has 4488 (32.4%) zerosZeros

Reproduction

Analysis started2024-06-04 21:09:26.234122
Analysis finished2024-06-04 21:09:50.408375
Duration24.17 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct13840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27677.5
Minimum20758
Maximum34597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size108.3 KiB
2024-06-04T17:09:50.610258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20758
5-th percentile21449.95
Q124217.75
median27677.5
Q331137.25
95-th percentile33905.05
Maximum34597
Range13839
Interquartile range (IQR)6919.5

Descriptive statistics

Standard deviation3995.4082
Coefficient of variation (CV)0.14435582
Kurtosis-1.2
Mean27677.5
Median Absolute Deviation (MAD)3460
Skewness0
Sum3.830566 × 108
Variance15963287
MonotonicityStrictly increasing
2024-06-04T17:09:50.972234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20758 1
 
< 0.1%
29989 1
 
< 0.1%
29978 1
 
< 0.1%
29979 1
 
< 0.1%
29980 1
 
< 0.1%
29981 1
 
< 0.1%
29982 1
 
< 0.1%
29983 1
 
< 0.1%
29984 1
 
< 0.1%
29985 1
 
< 0.1%
Other values (13830) 13830
99.9%
ValueCountFrequency (%)
20758 1
< 0.1%
20759 1
< 0.1%
20760 1
< 0.1%
20761 1
< 0.1%
20762 1
< 0.1%
20763 1
< 0.1%
20764 1
< 0.1%
20765 1
< 0.1%
20766 1
< 0.1%
20767 1
< 0.1%
ValueCountFrequency (%)
34597 1
< 0.1%
34596 1
< 0.1%
34595 1
< 0.1%
34594 1
< 0.1%
34593 1
< 0.1%
34592 1
< 0.1%
34591 1
< 0.1%
34590 1
< 0.1%
34589 1
< 0.1%
34588 1
< 0.1%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size838.2 KiB
Female
6965 
Male
6875 

Length

Max length6
Median length6
Mean length5.0065029
Min length4

Characters and Unicode

Total characters69290
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 6965
50.3%
Male 6875
49.7%

Length

2024-06-04T17:09:51.271170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T17:09:51.584116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
female 6965
50.3%
male 6875
49.7%

Most occurring characters

ValueCountFrequency (%)
e 20805
30.0%
a 13840
20.0%
l 13840
20.0%
F 6965
 
10.1%
m 6965
 
10.1%
M 6875
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55450
80.0%
Uppercase Letter 13840
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20805
37.5%
a 13840
25.0%
l 13840
25.0%
m 6965
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 6965
50.3%
M 6875
49.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 69290
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20805
30.0%
a 13840
20.0%
l 13840
20.0%
F 6965
 
10.1%
m 6965
 
10.1%
M 6875
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20805
30.0%
a 13840
20.0%
l 13840
20.0%
F 6965
 
10.1%
m 6965
 
10.1%
M 6875
 
9.9%

Age
Real number (ℝ)

Distinct1539
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.95274
Minimum14
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size108.3 KiB
2024-06-04T17:09:51.933165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile18
Q120
median22.906342
Q326
95-th percentile37.063599
Maximum61
Range47
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.7998135
Coefficient of variation (CV)0.2421357
Kurtosis3.5403751
Mean23.95274
Median Absolute Deviation (MAD)3.093658
Skewness1.5722379
Sum331505.92
Variance33.637837
MonotonicityNot monotonic
2024-06-04T17:09:52.271365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 1320
 
9.5%
26 1257
 
9.1%
21 1073
 
7.8%
23 816
 
5.9%
19 579
 
4.2%
20 389
 
2.8%
22 339
 
2.4%
17 297
 
2.1%
33 135
 
1.0%
27 111
 
0.8%
Other values (1529) 7524
54.4%
ValueCountFrequency (%)
14 10
 
0.1%
15 1
 
< 0.1%
16 101
0.7%
16.093234 1
 
< 0.1%
16.129279 6
 
< 0.1%
16.172992 2
 
< 0.1%
16.184891 1
 
< 0.1%
16.198153 2
 
< 0.1%
16.240576 1
 
< 0.1%
16.270434 2
 
< 0.1%
ValueCountFrequency (%)
61 2
 
< 0.1%
56 6
 
< 0.1%
55.24625 3
 
< 0.1%
55.137881 5
 
< 0.1%
55.022494 9
0.1%
55 20
0.1%
52 1
 
< 0.1%
47.7061 4
 
< 0.1%
47.283374 1
 
< 0.1%
47 1
 
< 0.1%

Height
Real number (ℝ)

HIGH CORRELATION 

Distinct1739
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6989341
Minimum1.45
Maximum1.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size108.3 KiB
2024-06-04T17:09:52.685215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile1.5501186
Q11.631662
median1.7
Q31.76071
95-th percentile1.849425
Maximum1.98
Range0.53
Interquartile range (IQR)0.129048

Descriptive statistics

Standard deviation0.088760585
Coefficient of variation (CV)0.052244867
Kurtosis-0.53783252
Mean1.6989341
Median Absolute Deviation (MAD)0.066055
Skewness0.064471766
Sum23513.248
Variance0.0078784415
MonotonicityNot monotonic
2024-06-04T17:09:53.052008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 847
 
6.1%
1.65 539
 
3.9%
1.75 445
 
3.2%
1.6 404
 
2.9%
1.8 309
 
2.2%
1.62 228
 
1.6%
1.63 179
 
1.3%
1.72 175
 
1.3%
1.56 169
 
1.2%
1.66 158
 
1.1%
Other values (1729) 10387
75.1%
ValueCountFrequency (%)
1.45 1
 
< 0.1%
1.456346 1
 
< 0.1%
1.48 2
 
< 0.1%
1.481682 2
 
< 0.1%
1.483284 3
 
< 0.1%
1.486484 1
 
< 0.1%
1.491441 1
 
< 0.1%
1.498561 1
 
< 0.1%
1.5 142
1.0%
1.501993 6
 
< 0.1%
ValueCountFrequency (%)
1.98 2
 
< 0.1%
1.975663 1
 
< 0.1%
1.96 1
 
< 0.1%
1.947406 1
 
< 0.1%
1.942725 1
 
< 0.1%
1.931263 6
< 0.1%
1.930416 2
 
< 0.1%
1.93 14
0.1%
1.929387 1
 
< 0.1%
1.92 1
 
< 0.1%

Weight
Real number (ℝ)

HIGH CORRELATION 

Distinct1798
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.384504
Minimum39
Maximum165.05727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size108.3 KiB
2024-06-04T17:09:53.450444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile49
Q165
median83.952968
Q3111.15781
95-th percentile131.33942
Maximum165.05727
Range126.05727
Interquartile range (IQR)46.157811

Descriptive statistics

Standard deviation26.111819
Coefficient of variation (CV)0.29881521
Kurtosis-1.0127565
Mean87.384504
Median Absolute Deviation (MAD)22.952968
Skewness0.086603552
Sum1209401.5
Variance681.82707
MonotonicityNot monotonic
2024-06-04T17:09:53.873475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 631
 
4.6%
75 426
 
3.1%
50 407
 
2.9%
60 324
 
2.3%
70 282
 
2.0%
65 247
 
1.8%
45 212
 
1.5%
82 191
 
1.4%
78 184
 
1.3%
42 171
 
1.2%
Other values (1788) 10765
77.8%
ValueCountFrequency (%)
39 2
 
< 0.1%
39.101805 2
 
< 0.1%
39.371523 4
< 0.1%
39.695295 2
 
< 0.1%
39.850137 3
< 0.1%
40 6
< 0.1%
40.202773 2
 
< 0.1%
40.333463 1
 
< 0.1%
40.343463 1
 
< 0.1%
40.821515 2
 
< 0.1%
ValueCountFrequency (%)
165.057269 1
 
< 0.1%
160.935351 2
 
< 0.1%
160.639405 1
 
< 0.1%
155.872093 1
 
< 0.1%
155.242672 2
 
< 0.1%
154.618446 4
< 0.1%
153.959945 3
< 0.1%
153.149491 5
< 0.1%
152.720545 4
< 0.1%
152.567671 1
 
< 0.1%

family_history_with_overweight
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
True
11384 
False
2456 
ValueCountFrequency (%)
True 11384
82.3%
False 2456
 
17.7%
2024-06-04T17:09:54.152593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FAVC
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
True
12583 
False
 
1257
ValueCountFrequency (%)
True 12583
90.9%
False 1257
 
9.1%
2024-06-04T17:09:54.324808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FCVC
Real number (ℝ)

Distinct828
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4428979
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size108.3 KiB
2024-06-04T17:09:54.557379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.8507984
Q12
median2.358087
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53160644
Coefficient of variation (CV)0.21761304
Kurtosis-0.89468816
Mean2.4428979
Median Absolute Deviation (MAD)0.401951
Skewness-0.34263777
Sum33809.707
Variance0.28260541
MonotonicityNot monotonic
2024-06-04T17:09:54.919303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5173
37.4%
3 5043
36.4%
1 188
 
1.4%
2.9673 81
 
0.6%
2.766612 40
 
0.3%
2.938616 29
 
0.2%
2.57649 28
 
0.2%
2.722161 24
 
0.2%
2.5621 22
 
0.2%
2.9553 21
 
0.2%
Other values (818) 3191
23.1%
ValueCountFrequency (%)
1 188
1.4%
1.003566 6
 
< 0.1%
1.005578 3
 
< 0.1%
1.00876 5
 
< 0.1%
1.016254 2
 
< 0.1%
1.031149 14
 
0.1%
1.036159 3
 
< 0.1%
1.036414 5
 
< 0.1%
1.036613 1
 
< 0.1%
1.052699 7
 
0.1%
ValueCountFrequency (%)
3 5043
36.4%
2.998441 2
 
< 0.1%
2.997951 5
 
< 0.1%
2.997524 3
 
< 0.1%
2.996717 5
 
< 0.1%
2.996186 5
 
< 0.1%
2.995599 3
 
< 0.1%
2.99448 6
 
< 0.1%
2.992329 2
 
< 0.1%
2.992205 9
 
0.1%

NCP
Real number (ℝ)

Distinct649
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7506103
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size108.3 KiB
2024-06-04T17:09:55.193436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q33
95-th percentile3.390143
Maximum4
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.71092686
Coefficient of variation (CV)0.2584615
Kurtosis1.7007587
Mean2.7506103
Median Absolute Deviation (MAD)0
Skewness-1.5381359
Sum38068.446
Variance0.505417
MonotonicityNot monotonic
2024-06-04T17:09:55.511992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 9745
70.4%
1 1351
 
9.8%
4 475
 
3.4%
1.894384 20
 
0.1%
2.992606 18
 
0.1%
2.993623 18
 
0.1%
2.806298 18
 
0.1%
2.993634 17
 
0.1%
1.971472 17
 
0.1%
2.911568 15
 
0.1%
Other values (639) 2146
 
15.5%
ValueCountFrequency (%)
1 1351
9.8%
1.000283 12
 
0.1%
1.000414 2
 
< 0.1%
1.00061 5
 
< 0.1%
1.001383 9
 
0.1%
1.001542 5
 
< 0.1%
1.001633 2
 
< 0.1%
1.005391 1
 
< 0.1%
1.009426 1
 
< 0.1%
1.010319 7
 
0.1%
ValueCountFrequency (%)
4 475
3.4%
3.999591 3
 
< 0.1%
3.998766 2
 
< 0.1%
3.998618 3
 
< 0.1%
3.995957 4
 
< 0.1%
3.995147 3
 
< 0.1%
3.994588 4
 
< 0.1%
3.990925 2
 
< 0.1%
3.98955 2
 
< 0.1%
3.989492 1
 
< 0.1%

CAEC
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size891.5 KiB
Sometimes
11689 
Frequently
1617 
Always
 
359
no
 
175

Length

Max length10
Median length9
Mean length8.9505058
Min length2

Characters and Unicode

Total characters123875
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowSometimes
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes 11689
84.5%
Frequently 1617
 
11.7%
Always 359
 
2.6%
no 175
 
1.3%

Length

2024-06-04T17:09:55.832449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T17:09:56.082480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 11689
84.5%
frequently 1617
 
11.7%
always 359
 
2.6%
no 175
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 26612
21.5%
m 23378
18.9%
t 13306
10.7%
s 12048
9.7%
o 11864
9.6%
S 11689
9.4%
i 11689
9.4%
y 1976
 
1.6%
l 1976
 
1.6%
n 1792
 
1.4%
Other values (7) 7545
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 110210
89.0%
Uppercase Letter 13665
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 26612
24.1%
m 23378
21.2%
t 13306
12.1%
s 12048
10.9%
o 11864
10.8%
i 11689
10.6%
y 1976
 
1.8%
l 1976
 
1.8%
n 1792
 
1.6%
r 1617
 
1.5%
Other values (4) 3952
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
S 11689
85.5%
F 1617
 
11.8%
A 359
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 123875
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 26612
21.5%
m 23378
18.9%
t 13306
10.7%
s 12048
9.7%
o 11864
9.6%
S 11689
9.4%
i 11689
9.4%
y 1976
 
1.6%
l 1976
 
1.6%
n 1792
 
1.4%
Other values (7) 7545
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 26612
21.5%
m 23378
18.9%
t 13306
10.7%
s 12048
9.7%
o 11864
9.6%
S 11689
9.4%
i 11689
9.4%
y 1976
 
1.6%
l 1976
 
1.6%
n 1792
 
1.4%
Other values (7) 7545
 
6.1%

SMOKE
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
False
13660 
True
 
180
ValueCountFrequency (%)
False 13660
98.7%
True 180
 
1.3%
2024-06-04T17:09:56.277398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

CH2O
Real number (ℝ)

Distinct1366
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0320441
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size108.3 KiB
2024-06-04T17:09:56.640968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.771781
median2
Q32.552388
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0.780607

Descriptive statistics

Standard deviation0.61122951
Coefficient of variation (CV)0.3007954
Kurtosis-0.75716675
Mean2.0320441
Median Absolute Deviation (MAD)0.4294355
Skewness-0.19886755
Sum28123.49
Variance0.37360152
MonotonicityNot monotonic
2024-06-04T17:09:56.914410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4306
31.1%
1 1852
 
13.4%
3 1130
 
8.2%
2.825629 52
 
0.4%
2.619517 49
 
0.4%
2.868167 37
 
0.3%
1.636326 37
 
0.3%
2.70485 35
 
0.3%
2.625537 34
 
0.2%
2.770125 31
 
0.2%
Other values (1356) 6277
45.4%
ValueCountFrequency (%)
1 1852
13.4%
1.000463 1
 
< 0.1%
1.000536 4
 
< 0.1%
1.000544 10
 
0.1%
1.000695 1
 
< 0.1%
1.001995 4
 
< 0.1%
1.002292 2
 
< 0.1%
1.003063 5
 
< 0.1%
1.003563 6
 
< 0.1%
1.003636 1
 
< 0.1%
ValueCountFrequency (%)
3 1130
8.2%
2.999495 2
 
< 0.1%
2.994515 1
 
< 0.1%
2.993623 1
 
< 0.1%
2.991671 6
 
< 0.1%
2.989389 2
 
< 0.1%
2.988771 5
 
< 0.1%
2.987718 2
 
< 0.1%
2.987406 2
 
< 0.1%
2.984323 1
 
< 0.1%

SCC
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
False
13376 
True
 
464
ValueCountFrequency (%)
False 13376
96.6%
True 464
 
3.4%
2024-06-04T17:09:57.187281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FAF
Real number (ℝ)

ZEROS 

Distinct1260
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97453248
Minimum0
Maximum3
Zeros3434
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size108.3 KiB
2024-06-04T17:09:57.454245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.001086
median1
Q31.571865
95-th percentile2.595128
Maximum3
Range3
Interquartile range (IQR)1.570779

Descriptive statistics

Standard deviation0.84036111
Coefficient of variation (CV)0.86232232
Kurtosis-0.46562553
Mean0.97453248
Median Absolute Deviation (MAD)0.8791165
Skewness0.5287328
Sum13487.529
Variance0.7062068
MonotonicityNot monotonic
2024-06-04T17:09:57.756126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3434
24.8%
1 2773
20.0%
2 1571
 
11.4%
3 561
 
4.1%
1.097905 45
 
0.3%
0.01586 30
 
0.2%
1.541072 27
 
0.2%
1.999836 24
 
0.2%
1.082236 22
 
0.2%
1.427037 20
 
0.1%
Other values (1250) 5333
38.5%
ValueCountFrequency (%)
0 3434
24.8%
9.6 × 10-52
 
< 0.1%
0.000272 10
 
0.1%
0.000454 3
 
< 0.1%
0.001015 10
 
0.1%
0.001086 4
 
< 0.1%
0.001272 6
 
< 0.1%
0.001297 7
 
0.1%
0.00203 5
 
< 0.1%
0.00342 4
 
< 0.1%
ValueCountFrequency (%)
3 561
4.1%
2.999918 1
 
< 0.1%
2.973499 1
 
< 0.1%
2.971832 3
 
< 0.1%
2.939733 3
 
< 0.1%
2.936551 4
 
< 0.1%
2.931527 2
 
< 0.1%
2.892922 14
 
0.1%
2.891986 3
 
< 0.1%
2.89118 6
 
< 0.1%

TUE
Real number (ℝ)

ZEROS 

Distinct1172
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61103302
Minimum0
Maximum2
Zeros4488
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size108.3 KiB
2024-06-04T17:09:58.077748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.552498
Q31
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6080053
Coefficient of variation (CV)0.99504492
Kurtosis-0.40770736
Mean0.61103302
Median Absolute Deviation (MAD)0.454738
Skewness0.69948648
Sum8456.697
Variance0.36967045
MonotonicityNot monotonic
2024-06-04T17:09:58.454830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4488
32.4%
1 2883
20.8%
2 800
 
5.8%
0.0026 54
 
0.4%
0.088236 42
 
0.3%
0.15171 38
 
0.3%
0.096576 36
 
0.3%
0.723154 36
 
0.3%
0.094213 32
 
0.2%
0.25115 31
 
0.2%
Other values (1162) 5400
39.0%
ValueCountFrequency (%)
0 4488
32.4%
7.3 × 10-53
 
< 0.1%
0.000355 1
 
< 0.1%
0.000436 4
 
< 0.1%
0.00133 9
 
0.1%
0.001337 1
 
< 0.1%
0.001518 7
 
0.1%
0.00158 1
 
< 0.1%
0.00159 4
 
< 0.1%
0.00164 9
 
0.1%
ValueCountFrequency (%)
2 800
5.8%
1.99219 5
 
< 0.1%
1.990617 3
 
< 0.1%
1.983678 3
 
< 0.1%
1.980875 6
 
< 0.1%
1.978043 4
 
< 0.1%
1.976894 1
 
< 0.1%
1.972926 1
 
< 0.1%
1.97117 3
 
< 0.1%
1.969507 6
 
< 0.1%

CALC
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size868.5 KiB
Sometimes
9979 
no
3513 
Frequently
 
346
Always
 
2

Length

Max length10
Median length9
Mean length7.2477601
Min length2

Characters and Unicode

Total characters100309
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowSometimes
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes 9979
72.1%
no 3513
 
25.4%
Frequently 346
 
2.5%
Always 2
 
< 0.1%

Length

2024-06-04T17:09:58.877335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T17:09:59.130866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 9979
72.1%
no 3513
 
25.4%
frequently 346
 
2.5%
always 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 20650
20.6%
m 19958
19.9%
o 13492
13.5%
t 10325
10.3%
s 9981
10.0%
S 9979
9.9%
i 9979
9.9%
n 3859
 
3.8%
y 348
 
0.3%
l 348
 
0.3%
Other values (7) 1390
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 89982
89.7%
Uppercase Letter 10327
 
10.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20650
22.9%
m 19958
22.2%
o 13492
15.0%
t 10325
11.5%
s 9981
11.1%
i 9979
11.1%
n 3859
 
4.3%
y 348
 
0.4%
l 348
 
0.4%
u 346
 
0.4%
Other values (4) 696
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
S 9979
96.6%
F 346
 
3.4%
A 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 100309
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20650
20.6%
m 19958
19.9%
o 13492
13.5%
t 10325
10.3%
s 9981
10.0%
S 9979
9.9%
i 9979
9.9%
n 3859
 
3.8%
y 348
 
0.3%
l 348
 
0.3%
Other values (7) 1390
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20650
20.6%
m 19958
19.9%
o 13492
13.5%
t 10325
10.3%
s 9981
10.0%
S 9979
9.9%
i 9979
9.9%
n 3859
 
3.8%
y 348
 
0.3%
l 348
 
0.3%
Other values (7) 1390
 
1.4%

MTRANS
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Public_Transportation
11111 
Automobile
2405 
Walking
 
280
Bike
 
25
Motorbike
 
19

Length

Max length21
Median length21
Mean length18.758092
Min length4

Characters and Unicode

Total characters259612
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic_Transportation
2nd rowPublic_Transportation
3rd rowPublic_Transportation
4th rowPublic_Transportation
5th rowPublic_Transportation

Common Values

ValueCountFrequency (%)
Public_Transportation 11111
80.3%
Automobile 2405
 
17.4%
Walking 280
 
2.0%
Bike 25
 
0.2%
Motorbike 19
 
0.1%

Length

2024-06-04T17:09:59.437139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T17:09:59.701826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
public_transportation 11111
80.3%
automobile 2405
 
17.4%
walking 280
 
2.0%
bike 25
 
0.2%
motorbike 19
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 27070
10.4%
i 24951
 
9.6%
t 24646
 
9.5%
a 22502
 
8.7%
n 22502
 
8.7%
r 22241
 
8.6%
l 13796
 
5.3%
b 13535
 
5.2%
u 13516
 
5.2%
P 11111
 
4.3%
Other values (13) 63742
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 223550
86.1%
Uppercase Letter 24951
 
9.6%
Connector Punctuation 11111
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 27070
12.1%
i 24951
11.2%
t 24646
11.0%
a 22502
10.1%
n 22502
10.1%
r 22241
9.9%
l 13796
6.2%
b 13535
6.1%
u 13516
6.0%
p 11111
5.0%
Other values (6) 27680
12.4%
Uppercase Letter
ValueCountFrequency (%)
P 11111
44.5%
T 11111
44.5%
A 2405
 
9.6%
W 280
 
1.1%
B 25
 
0.1%
M 19
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 11111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 248501
95.7%
Common 11111
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 27070
10.9%
i 24951
10.0%
t 24646
9.9%
a 22502
9.1%
n 22502
9.1%
r 22241
9.0%
l 13796
 
5.6%
b 13535
 
5.4%
u 13516
 
5.4%
P 11111
 
4.5%
Other values (12) 52631
21.2%
Common
ValueCountFrequency (%)
_ 11111
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 259612
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 27070
10.4%
i 24951
 
9.6%
t 24646
 
9.5%
a 22502
 
8.7%
n 22502
 
8.7%
r 22241
 
8.6%
l 13796
 
5.3%
b 13535
 
5.2%
u 13516
 
5.2%
P 11111
 
4.3%
Other values (13) 63742
24.6%

Interactions

2024-06-04T17:09:47.123573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:29.077821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:31.346252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:33.516296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:36.000189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:38.115691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:40.355684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:42.619777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:44.993228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:47.310895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:29.435326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:31.549949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:33.688765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:36.228669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:38.329694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:40.609085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:42.855808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:45.215295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:47.496413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:29.567628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:31.770869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:34.126961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:36.457419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:38.621957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:40.816146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:43.152630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:45.427773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:47.725033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:29.863110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:32.053104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:34.397433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:36.694751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:38.829466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:41.000618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:43.453367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:45.714084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:47.971631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:30.128252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:32.319253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:34.707224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:36.934177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:39.057872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:41.274761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:43.682013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:45.938684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:48.156682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:30.329318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:32.480833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:34.995069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:37.136955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:39.317150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:41.561742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:43.934134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:46.202498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:48.412325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:30.546017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:32.742833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:35.207858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:37.339272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:39.594151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:41.844119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:44.208161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:46.423995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:48.700108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:30.818361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:32.987402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:35.439646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:37.616789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:39.825502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:42.099353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:44.472490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:46.649348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:48.950444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:31.113348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:33.237488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:35.666928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:37.860162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:40.065578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:42.318097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:44.768674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-04T17:09:46.821949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-06-04T17:09:59.912584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idAgeHeightWeightFCVCNCPCH2OFAFTUEGenderfamily_history_with_overweightFAVCCAECSMOKESCCCALCMTRANS
id1.000-0.006-0.009-0.0090.010-0.0040.007-0.0010.0030.0000.0000.0190.0000.0000.0000.0080.000
Age-0.0061.0000.0000.4420.103-0.1210.087-0.268-0.3160.2400.3050.1390.1550.1250.1260.1680.373
Height-0.0090.0001.0000.412-0.1300.1070.1810.3160.0710.6500.2900.1600.1270.1290.1230.0960.080
Weight-0.0090.4420.4121.0000.216-0.0270.336-0.076-0.0920.5090.5880.2340.3140.0750.2030.2390.164
FCVC0.0100.103-0.1300.2161.0000.1170.101-0.114-0.1160.3960.1240.0960.0870.0220.0380.1420.096
NCP-0.004-0.1210.107-0.0270.1171.0000.1030.0990.1340.1530.1900.0590.1630.0000.0260.1100.059
CH2O0.0070.0870.1810.3360.1010.1031.0000.0440.0070.3430.2630.1710.1590.0500.0750.1490.103
FAF-0.001-0.2680.316-0.076-0.1140.0990.0441.000-0.0030.3650.1810.1400.1140.0330.0570.1290.091
TUE0.003-0.3160.071-0.092-0.1160.1340.007-0.0031.0000.2120.2060.1440.1240.0350.0570.1320.134
Gender0.0000.2400.6500.5090.3960.1530.3430.3650.2121.0000.1030.0210.0780.0670.0560.0840.127
family_history_with_overweight0.0000.3050.2900.5880.1240.1900.2630.1810.2060.1031.0000.1500.3270.0110.1750.0080.113
FAVC0.0190.1390.1600.2340.0960.0590.1710.1400.1440.0210.1501.0000.1380.0210.1040.1090.104
CAEC0.0000.1550.1270.3140.0870.1630.1590.1140.1240.0780.3270.1381.0000.0400.1270.0780.066
SMOKE0.0000.1250.1290.0750.0220.0000.0500.0330.0350.0670.0110.0210.0401.0000.0210.0130.013
SCC0.0000.1260.1230.2030.0380.0260.0750.0570.0570.0560.1750.1040.1270.0211.0000.0160.044
CALC0.0080.1680.0960.2390.1420.1100.1490.1290.1320.0840.0080.1090.0780.0130.0161.0000.069
MTRANS0.0000.3730.0800.1640.0960.0590.1030.0910.1340.1270.1130.1040.0660.0130.0440.0691.000

Missing values

2024-06-04T17:09:49.265054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-04T17:09:50.080725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idGenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANS
020758Male26.8998861.848294120.644178yesyes2.9386163.000000Sometimesno2.825629no0.8554000.000000SometimesPublic_Transportation
120759Female21.0000001.60000066.000000yesyes2.0000001.000000Sometimesno3.000000no1.0000000.000000SometimesPublic_Transportation
220760Female26.0000001.643355111.600553yesyes3.0000003.000000Sometimesno2.621877no0.0000000.250502SometimesPublic_Transportation
320761Male20.9792541.553127103.669116yesyes2.0000002.977909Sometimesno2.786417no0.0948510.000000SometimesPublic_Transportation
420762Female26.0000001.627396104.835346yesyes3.0000003.000000Sometimesno2.653531no0.0000000.741069SometimesPublic_Transportation
520763Male19.7990541.84475159.605028yesyes2.0000004.000000Sometimesno2.722063no2.0000001.283673noAutomobile
620764Male18.0000001.72883451.442293yesyes1.2020753.000000Sometimesno1.087166no0.7885851.000000SometimesPublic_Transportation
720765Male20.0000001.81000080.000000yesyes2.0000003.000000Alwaysno2.000000no3.0000000.000000noAutomobile
820766Male21.0000001.70000080.000000yesyes2.0000003.000000Frequentlyyes2.000000no0.0000002.000000noPublic_Transportation
920767Female21.0000001.56000053.000000noyes2.0000003.000000Sometimesno2.000000no0.0000001.000000SometimesPublic_Transportation
idGenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANS
1383034588Female18.0000001.62000084.000000yesyes2.0000003.000000Sometimesno3.000000no1.0000000.000000noPublic_Transportation
1383134589Female26.0000001.62000056.000000yesyes3.0000003.000000Sometimesno2.000000no0.0000000.000000noAutomobile
1383234590Female21.6801231.737056132.262558yesyes3.0000003.000000Sometimesno1.676975no1.5376390.858059SometimesPublic_Transportation
1383334591Female19.9945431.68110049.838965nono3.0000003.765526Frequentlyno1.000000no1.9955820.000000SometimesPublic_Transportation
1383434592Female16.8348131.53561843.919835yesyes2.8825221.000000Sometimesno2.434832no0.0000001.191998noPublic_Transportation
1383534593Male23.3278361.72138478.030383yesno2.8132343.000000Sometimesno1.000000no0.8070760.778632SometimesPublic_Transportation
1383634594Female29.0000001.59000062.000000noyes3.0000003.000000Sometimesno2.000000no0.0000000.000000SometimesPublic_Transportation
1383734595Female22.9356121.58554744.376637noyes3.0000002.273740Frequentlyno2.000000no1.9498401.000000SometimesPublic_Transportation
1383834596Male21.0000001.62000053.000000yesyes2.0000003.000000Sometimesno2.000000no3.0000002.000000noPublic_Transportation
1383934597Male26.4909261.812259120.980508yesyes2.7449943.000000Sometimesno2.205977no1.3042910.630866SometimesPublic_Transportation